Lab 11 - Interactive Visualization

library(data.table)
library(tidyverse)

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## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.1     ✓ forcats 0.5.1

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library(dplyr)
library(plotly)

## 
## Attaching package: 'plotly'

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library(DT)
library(knitr)
opts_chunk$set(
  warning = FALSE,
  message = FALSE,
  eval=TRUE,
  echo = TRUE,
  fig.width = 7, 
  fig.align = 'center',
  fig.asp = 0.618,
  out.width = "700px")

Learning Goals

Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo() # Lab Description We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.

The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv” Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv”” Merge datasets

1. Read in the data

## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
# load COVID state-level data from NYT
### FINISH THE CODE HERE ###
cv_states_readin <- as.data.frame(fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv") )
# load state population data
### FINISH THE CODE HERE ###
state_pops_readin <- as.data.frame(fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops <- state_pops_readin
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
### FINISH THE CODE HERE ###
cv_states <- merge( cv_states_readin, state_pops, by="state")

2. Look at the data

Inspect the dimensions, head, and tail of the data Inspect the structure of each variables. Are they in the correct format?

dim(cv_states)

## [1] 32250     9

head(cv_states)

##     state       date fips  cases deaths geo_id population pop_density abb
## 1 Alabama 2020-06-10    1  21989    744      1    4887871    96.50939  AL
## 2 Alabama 2021-10-31    1 832047  15573      1    4887871    96.50939  AL
## 3 Alabama 2021-05-26    1 542831  11138      1    4887871    96.50939  AL
## 4 Alabama 2020-04-19    1   4903    160      1    4887871    96.50939  AL
## 5 Alabama 2021-07-07    1 552911  11387      1    4887871    96.50939  AL
## 6 Alabama 2020-06-21    1  30021    839      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips cases deaths geo_id population pop_density abb
## 32245 Wyoming 2020-12-01   56 33805    230     56     577737    5.950611  WY
## 32246 Wyoming 2021-08-15   56 68272    793     56     577737    5.950611  WY
## 32247 Wyoming 2021-03-16   56 55352    693     56     577737    5.950611  WY
## 32248 Wyoming 2021-04-12   56 56988    701     56     577737    5.950611  WY
## 32249 Wyoming 2021-04-01   56 56389    700     56     577737    5.950611  WY
## 32250 Wyoming 2020-11-30   56 33305    215     56     577737    5.950611  WY

str(cv_states)

## 'data.frame':    32250 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : IDate, format: "2020-06-10" "2021-10-31" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  21989 832047 542831 4903 552911 30021 117242 809485 134417 547135 ...
##  $ deaths     : int  744 15573 11138 160 11387 839 2037 14869 2285 11252 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

3. Format the data

Make date into a date variable Make state into a factor variable Order the data first by state, second by date Confirm the variables are now correctly formatted Inspect the range values for each variable. What is the date range? The range of cases and deaths?

date range: from “2020-01-21” to “2021-11-11” range of cases: from 1 to 973517
range of deaths: from 0 to 33305

# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

### FINISH THE CODE HERE 
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state,cv_states$date),]

# Confirm the variables are now correctly formatted
str(cv_states)

## 'data.frame':    32250 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...

head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 215 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 428 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 69  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 496 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 341 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 53  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips  cases deaths geo_id population pop_density abb
## 31735 Wyoming 2021-11-06   56 105318   1243     56     577737    5.950611  WY
## 31791 Wyoming 2021-11-07   56 105318   1243     56     577737    5.950611  WY
## 31967 Wyoming 2021-11-08   56 105990   1243     56     577737    5.950611  WY
## 31942 Wyoming 2021-11-09   56 106287   1298     56     577737    5.950611  WY
## 31961 Wyoming 2021-11-10   56 106698   1298     56     577737    5.950611  WY
## 32016 Wyoming 2021-11-11   56 106698   1298     56     577737    5.950611  WY

# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 215 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 428 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 69  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 496 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 341 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 53  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

summary(cv_states)

##            state            date                 fips           cases        
##  Washington   :  661   Min.   :2020-01-21   Min.   : 1.00   Min.   :      1  
##  Illinois     :  658   1st Qu.:2020-08-03   1st Qu.:16.00   1st Qu.:  31774  
##  California   :  657   Median :2021-01-05   Median :29.00   Median : 146459  
##  Arizona      :  656   Mean   :2021-01-05   Mean   :29.78   Mean   : 386609  
##  Massachusetts:  650   3rd Qu.:2021-06-09   3rd Qu.:44.00   3rd Qu.: 481786  
##  Wisconsin    :  646   Max.   :2021-11-11   Max.   :72.00   Max.   :4993930  
##  (Other)      :28322                                                         
##      deaths          geo_id        population        pop_density       
##  Min.   :    0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
##  1st Qu.:  621   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
##  Median : 2658   Median :29.00   Median : 4468402   Median :  107.860  
##  Mean   : 7155   Mean   :29.78   Mean   : 6433897   Mean   :  422.513  
##  3rd Qu.: 8432   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
##  Max.   :73132   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
##                                                     NA's   :609        
##       abb       
##  WA     :  661  
##  IL     :  658  
##  CA     :  657  
##  AZ     :  656  
##  MA     :  650  
##  WI     :  646  
##  (Other):28322

min(cv_states$date)

## [1] "2020-01-21"

max(cv_states$date)

## [1] "2021-11-11"

4. Add new_cases and new_deaths and correct outliers

Add variables for new cases, new_cases, and new deaths, new_deaths:

Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2 Filter to dates after June 1, 2021

Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?

Correct outliers: Set negative values for new_cases or new_deaths to 0

Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths

Get the rolling average of new cases and new deaths to smooth over time

Inspect data again interactively

# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]
  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]
  #### FINISH THE CODE HERE ###
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] 
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# Inspect outliers in new_cases and new_deaths using plotly
### FINISH THE CODE HERE ###
p1<-ggplot(cv_states, 
           aes( x=date, y=new_cases, color=state  )
           ) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)

p1<-NULL # to clear from workspace
### FINISH THE CODE HERE ###
p2<-ggplot(cv_states, 
           aes(x=date, y=new_deaths, color=state )
           ) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updates `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

5. Add additional variables

• Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:

  • per100k = cases per 100,000 population
  • newper100k= new cases per 100,000
  • deathsper100k = deaths per 100,000
  • newdeathsper100k = new deaths per 100,000

• Add a “naive CFR” variable representing deaths / cases on each date for each state

• Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
### FINISH THE CODE HERE ###
max_date <- max(cv_states$date)
cv_states_today = cv_states %>% filter(date==as.Date(max_date))

II. Interactive plots

6. Explore scatterplots using plot_ly()

• Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
  • Use hover to identify any outliers.
  • Remove those outliers and replot.
• Choose one plot. For this plot:
  • Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
  • Add layout information to title the chart and the axes
  • Enable hovermode = "compare"

# pop_density vs. cases
### FINISH THE CODE HERE ###
cv_states_today %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# filter out "District of Columbia"
cv_states_today_scatter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_scatter %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# pop_density vs. deathsper100k
cv_states_today_scatter %>% 
  plot_ly(x = ~pop_density, y = ~newdeathsper100k, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# Adding hoverinfo
cv_states_today_scatter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ",
                        deathsper100k, sep=""), sep = "<br>")) %>%
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
                  yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
         hovermode = "compare")

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

• For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()

• What’s the geom_*() we need here? we use geom_point hear.

• Explore the pattern between \(x\) and \(y\) using geom_smooth()

  • Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?

  They might be very weakly negatively correlated, but not very clear from the plot.

### FINISH THE CODE HERE ###
p <- ggplot(cv_states_today_scatter, aes(x=pop_density, y=per100k, color=state, size=population)) + geom_point() + geom_smooth() 
ggplotly(p)

8. Multiple line chart

• Create a line chart of the naive_CFR for all states over time using plot_ly()
  • Use hoverinfo to identify states that had a “first peak”: the washingtion state
  • Use the zoom and pan tools to inspect the naive_CFR for the states that had a “first peak” in September. How have they changed over time? Connecticut state. It almost kept constant.
• Create one more line chart, for Texas only, which shows new_cases and new_deaths together in one plot. Hint: use add_lines()

  • Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?

    the peak of cases: Sep 21, the peak of deaths: Juy 27, the time delay: nearly two months.

# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")

# Line chart for Texas showing new_cases and new_deaths together
### FINISH THE CODE HERE ###
cv_states %>% filter(state=="Texas") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")

9. Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than April 1st, 2020 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix

• Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks

# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% filter(date>as.Date("2020-04-01"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2020-04-01"), as.Date("2020-10-01"), by="2 weeks")
### FINISH THE CODE HERE ### 
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% filter( date %in% filter_dates )
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

10. Map

• Create a map to visualize the naive_CFR by state on May 1st, 2020
• Compare with a map visualizing the naive_CFR by state on most recent date
• Plot the two maps side by side using subplot(). Make sure the shading is for the same range of values (google is your friend for this)
• Describe the difference in the pattern of the CFR.

### For May 1 2020
# Extract the data for each state by its abbreviation
cv_CFR <- cv_states %>% filter(date=="2020-05-01") %>% select(state, abb, naive_CFR, cases, deaths) # select data
cv_CFR$state_name <- cv_CFR$state
cv_CFR$state <- cv_CFR$abb
cv_CFR$abb <- NULL
# Create hover text
cv_CFR$hover <- with(cv_CFR, paste(state_name, '<br>', "CFR: ", naive_CFR, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 9
# Create the map
fig <- plot_geo(cv_CFR, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~naive_CFR, text = ~hover, locations = ~state,
    color = ~naive_CFR, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "CFR May 1 2020", limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('CFR by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_May1 <- fig
#############
### For Today
# Extract the data for each state by its abbreviation
cv_CFR <- cv_states_today %>%  select(state, abb, naive_CFR, cases, deaths) # select data
cv_CFR$state_name <- cv_CFR$state
cv_CFR$state <- cv_CFR$abb
cv_CFR$abb <- NULL
# Create hover text
cv_CFR$hover <- with(cv_CFR, paste(state_name, '<br>', "CFR: ", naive_CFR, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
# Create the map
fig <- plot_geo(cv_CFR, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~naive_CFR, text = ~hover, locations = ~state,
    color = ~naive_CFR, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "CFR May 1 2020", limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('CFR by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig
### Plot side by side 
### FINISH THE CODE HERE ###
subplot( fig_May1, fig_Today )